With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

In recent years, deep learning (DL) has been the most popular computational approach in
the field of machine learning (ML), achieving exceptional results on a variety of complex …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the exploration of …

Modeling users' dynamic preferences from their historical behaviors is challenging and
crucial for recommendation systems. Previous methods employ sequential neural networks …

Sequential dynamics are a key feature of many modern recommender systems, which seek
to capture the'context'of users' activities on the basis of actions they have performed recently …

With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing
users and items into latent representation space, with their correlative patterns from …

Knowledge Graphs (KGs) have been utilized as useful side information to improve
recommendation quality. In those recommender systems, knowledge graph information …

YouTube represents one of the largest scale and most sophisticated industrial
recommendation systems in existence. In this paper, we describe the system at a high level …